57 citations · 236 across the 22 of their papers we have counts for
7 papers · 1 filter
VSCode: General Visual Salient and Camouflaged Object Detection with 2D Prompt Learning
Ziyang Luo, Nian Liu, Wangbo Zhao +5
Salient object detection (SOD) and camouflaged object detection (COD) are related yet distinct binary mapping tasks. These tasks involve multiple modalities, sharing commonalities…
GP-NeRF: Generalized Perception NeRF for Context-Aware 3D Scene Understanding
Hao Li, Dingwen Zhang, Yalun Dai +5
Applying NeRF to downstream perception tasks for scene understanding and representation is becoming increasingly popular. Most existing methods treat semantic prediction as an addi…
VST++: Efficient and Stronger Visual Saliency Transformer
Nian Liu, Ziyang Luo, Ni Zhang +1
While previous CNN-based models have exhibited promising results for salient object detection (SOD), their ability to explore global long-range dependencies is restricted. Our prev…
Multi-grained Temporal Prototype Learning for Few-shot Video Object Segmentation
Nian Liu, Kepan Nan, Wangbo Zhao +7
Few-Shot Video Object Segmentation (FSVOS) aims to segment objects in a query video with the same category defined by a few annotated support images. However, this task was seldom…
CalibNet: Dual-branch Cross-modal Calibration for RGB-D Salient Instance Segmentation
Jialun Pei, Tao Jiang, He Tang +4
We propose a novel approach for RGB-D salient instance segmentation using a dual-branch cross-modal feature calibration architecture called CalibNet. Our method simultaneously cali…
Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object Detection
Long Li, Junwei Han, Ni Zhang +5
Most previous co-salient object detection works mainly focus on extracting co-salient cues via mining the consistency relations across images while ignoring explicit exploration of…
Boosting Low-Data Instance Segmentation by Unsupervised Pre-training with Saliency Prompt
Hao Li, Dingwen Zhang, Nian Liu +5
Recently, inspired by DETR variants, query-based end-to-end instance segmentation (QEIS) methods have outperformed CNN-based models on large-scale datasets. Yet they would lose eff…